• Title/Summary/Keyword: Prediction of variables

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Development of Evaluation Model of Pumping and Drainage Station Using Performance Degradation Factors (농업기반시설물 양·배수장의 성능저하 요인분석 및 성능평가 모델 개발)

  • Lee, Jonghyuk;Lee, Sangik;Jeong, Youngjoon;Lee, Jemyung;Yoon, Seongsoo;Park, Jinseon;Lee, Byeongjoon;Lee, Joongu;Choi, Won
    • Journal of The Korean Society of Agricultural Engineers
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    • v.61 no.4
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    • pp.75-86
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    • 2019
  • Recently, natural disasters due to abnormal climates are frequently outbreaking, and there is rapid increase of damage to aged agricultural infrastructure. As agricultural infrastructure facilities are in contact with water throughout the year and the number of them is significant, it is important to build a maintenance management system. Especially, the current maintenance management system of pumping and drainage stations among the agricultural facilities has the limit of lack of objectivity and management personnel. The purpose of this study is to develop a performance evaluation model using the factors related to performance degradation of pumping and drainage facilities and to predict the performance of the facilities in response to climate change. In this study, we focused on the pumping and drainage stations belonging to each climatic zone separated by the Korea geographical climatic classification system. The performance evaluation model was developed using three different statistical models of POLS, RE, and LASSO. As the result of analysis of statistical models, LASSO was selected for the performance evaluation model as it solved the multicollinearity problem between variables, and showed the smallest MSE. To predict the performance degradation due to climate change, the climate change response variables were classified into three categories: climate exposure, sensitivity, and adaptive capacity. The performance degradation prediction was performed at each facility using the developed performance evaluation model and the climate change response variables.

Investigating Non-Laboratory Variables to Predict Diabetic and Prediabetic Patients from Electronic Medical Records Using Machine Learning

  • Mukhtar, Hamid;Al Azwari, Sana
    • International Journal of Computer Science & Network Security
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    • v.21 no.9
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    • pp.19-30
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    • 2021
  • Diabetes Mellitus (DM) is one of common chronic diseases leading to severe health complications that may cause death. The disease influences individuals, community, and the government due to the continuous monitoring, lifelong commitment, and the cost of treatment. The World Health Organization (WHO) considers Saudi Arabia as one of the top 10 countries in diabetes prevalence across the world. Since most of the medical services are provided by the government, the cost of the treatment in terms of hospitals and clinical visits and lab tests represents a real burden due to the large scale of the disease. The ability to predict the diabetic status of a patient without the laboratory tests by performing screening based on some personal features can lessen the health and economic burden caused by diabetes alone. The goal of this paper is to investigate the prediction of diabetic and prediabetic patients by considering factors other than the laboratory tests, as required by physicians in general. With the data obtained from local hospitals, medical records were processed to obtain a dataset that classified patients into three classes: diabetic, prediabetic, and non-diabetic. After applying three machine learning algorithms, we established good performance for accuracy, precision, and recall of the models on the dataset. Further analysis was performed on the data to identify important non-laboratory variables related to the patients for diabetes classification. The importance of five variables (gender, physical activity level, hypertension, BMI, and age) from the person's basic health data were investigated to find their contribution to the state of a patient being diabetic, prediabetic or normal. Our analysis presented great agreement with the risk factors of diabetes and prediabetes stated by the American Diabetes Association (ADA) and other health institutions worldwide. We conclude that by performing class-specific analysis of the disease, important factors specific to Saudi population can be identified, whose management can result in controlling the disease. We also provide some recommendations learnt from this research.

Prediction and factors of Seoul apartment price using convolutional neural networks (CNN 모형을 이용한 서울 아파트 가격 예측과 그 요인)

  • Lee, Hyunjae;Son, Donghui;Kim, Sujin;Oh, Sein;Kim, Jaejik
    • The Korean Journal of Applied Statistics
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    • v.33 no.5
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    • pp.603-614
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    • 2020
  • This study focuses on the prediction and factors of apartment prices in Seoul using a convolutional neural networks (CNN) model that has shown excellent performance as a predictive model of image data. To do this, we consider natural environmental factors, infrastructure factors, and social economic factors of the apartments as input variables of the CNN model. The natural environmental factors include rivers, green areas, and altitudes of apartments. The infrastructure factors have bus stops, subway stations, commercial districts, schools, and the social economic factors are the number of jobs and criminal rates, etc. We predict apartment prices and interpret the factors for the prices by converting the values of these input variables to play the same role as pixel values of image channels for the input layer in the CNN model. In addition, the CNN model used in this study takes into account the spatial characteristics of each apartment by describing the natural environmental and infrastructure factors variables as binary images centered on each apartment in each input layer.

Utilizing Artificial Neural Networks for Establishing Hearing-Loss Predicting Models Based on a Longitudinal Dataset and Their Implications for Managing the Hearing Conservation Program

  • Thanawat Khajonklin;Yih-Min Sun;Yue-Liang Leon Guo;Hsin-I Hsu;Chung Sik Yoon;Cheng-Yu Lin;Perng-Jy Tsai
    • Safety and Health at Work
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    • v.15 no.2
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    • pp.220-227
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    • 2024
  • Background: Though the artificial neural network (ANN) technique has been used to predict noise-induced hearing loss (NIHL), the established prediction models have primarily relied on cross-sectional datasets, and hence, they may not comprehensively capture the chronic nature of NIHL as a disease linked to long-term noise exposure among workers. Methods: A comprehensive dataset was utilized, encompassing eight-year longitudinal personal hearing threshold levels (HTLs) as well as information on seven personal variables and two environmental variables to establish NIHL predicting models through the ANN technique. Three subdatasets were extracted from the afirementioned comprehensive dataset to assess the advantages of the present study in NIHL predictions. Results: The dataset was gathered from 170 workers employed in a steel-making industry, with a median cumulative noise exposure and HTL of 88.40 dBA-year and 19.58 dB, respectively. Utilizing the longitudinal dataset demonstrated superior prediction capabilities compared to cross-sectional datasets. Incorporating the more comprehensive dataset led to improved NIHL predictions, particularly when considering variables such as noise pattern and use of personal protective equipment. Despite fluctuations observed in the measured HTLs, the ANN predicting models consistently revealed a discernible trend. Conclusions: A consistent correlation was observed between the measured HTLs and the results obtained from the predicting models. However, it is essential to exercise caution when utilizing the model-predicted NIHLs for individual workers due to inherent personal fluctuations in HTLs. Nonetheless, these ANN models can serve as a valuable reference for the industry in effectively managing its hearing conservation program.

A study on improving the accuracy of machine learning models through the use of non-financial information in predicting the Closure of operator using electronic payment service (전자결제서비스 이용 사업자 폐업 예측에서 비재무정보 활용을 통한 머신러닝 모델의 정확도 향상에 관한 연구)

  • Hyunjeong Gong;Eugene Hwang;Sunghyuk Park
    • Journal of Intelligence and Information Systems
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    • v.29 no.3
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    • pp.361-381
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    • 2023
  • Research on corporate bankruptcy prediction has been focused on financial information. Since the company's financial information is updated quarterly, there is a problem that timeliness is insufficient in predicting the possibility of a company's business closure in real time. Evaluated companies that want to improve this need a method of judging the soundness of a company that uses information other than financial information to judge the soundness of a target company. To this end, as information technology has made it easier to collect non-financial information about companies, research has been conducted to apply additional variables and various methodologies other than financial information to predict corporate bankruptcy. It has become an important research task to determine whether it has an effect. In this study, we examined the impact of electronic payment-related information, which constitutes non-financial information, when predicting the closure of business operators using electronic payment service and examined the difference in closure prediction accuracy according to the combination of financial and non-financial information. Specifically, three research models consisting of a financial information model, a non-financial information model, and a combined model were designed, and the closure prediction accuracy was confirmed with six algorithms including the Multi Layer Perceptron (MLP) algorithm. The model combining financial and non-financial information showed the highest prediction accuracy, followed by the non-financial information model and the financial information model in order. As for the prediction accuracy of business closure by algorithm, XGBoost showed the highest prediction accuracy among the six algorithms. As a result of examining the relative importance of a total of 87 variables used to predict business closure, it was confirmed that more than 70% of the top 20 variables that had a significant impact on the prediction of business closure were non-financial information. Through this, it was confirmed that electronic payment-related information of non-financial information is an important variable in predicting business closure, and the possibility of using non-financial information as an alternative to financial information was also examined. Based on this study, the importance of collecting and utilizing non-financial information as information that can predict business closure is recognized, and a plan to utilize it for corporate decision-making is also proposed.

Effect of Colorimetric Characteristics and Tone Combination on Color Emotion Factors of Naturally Dyed Color Combination Fabrics -Focus on Yellowish and Reddish Fabrics- (천연염색 배색직물의 색채 특성과 톤 조합이 색채감성요인에 미치는 영향 -황색과 적색계열을 중심으로-)

  • Lee, An Rye;Sarmandakh, Badmaanyambuu;Kang, Eun Young;Yi, Eunjou
    • Journal of the Korean Society of Clothing and Textiles
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    • v.36 no.10
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    • pp.1028-1039
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    • 2012
  • This study identified color emotion factors of naturally dyed two-color combination fabrics focused on yellowish and reddish shades to examine the relationship between color emotion factors and physical colorimetric variables (as well as tone combination groups) to provide prediction models for color emotion factors of naturally dyed fabrics with a two-color combination. Each of eight different stimuli were prepared by paring two pieces of silk fabrics colored in red and yellow by natural dyeing respectively; in addition, their color emotion descriptors were evaluated by human subjects using semantic deferential scales. 'Joyful', 'Natural', 'Classical', and 'Soft' were extracted as color emotion factors for the naturally dyed yellowish-reddish combination fabrics. They were found to be significantly affected by physical colorimetric variables such as CIE C and L and tone combination groups. Finally, prediction models for all color emotion factors were established using physical colorimetric variables and tone combination groups that led to the conclusion that they could be applicable to design a color combination for naturally dyed fashion fabrics.

Development of Long-Term Hospitalization Prediction Model for Minor Automobile Accident Patients (자동차 사고 경상환자의 장기입원 예측 모델 개발)

  • DoegGyu Lee;DongHyun Nam;Sung-Phil Heo
    • Journal of Korea Society of Industrial Information Systems
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    • v.28 no.6
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    • pp.11-20
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    • 2023
  • The cost of medical treatment for motor vehicle accidents is increasing every year. In this study, we created a model to predict long-term hospitalization(more than 18 days) among minor patients, which is the main item of increasing traffic accident medical expenses, using five algorithms such as decision tree, and analyzed the factors affecting long-term hospitalization. As a result, the accuracy of the prediction models ranged from 91.377 to 91.451, and there was no significant difference between each model, but the random forest and XGBoost models had the highest accuracy of 91.451. There were significant differences between models in the importance of explanatory variables, such as hospital location, name of disease, and type of hospital, between the long-stay and non-long-stay groups. Model validation was tested by comparing the average accuracy of each model cross-validated(10 times) on the training data with the accuracy of the validation data. To test of the explanatory variables, the chi-square test was used for categorical variables.

Analsis Of Outliers In Real Estate Prices Using Autoencoder (Autoencoder 기법을 활용한 부동산 가격 이상치 분석)

  • Kim, Yoonseo;Park, Jongchan;Oh, Hayoung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.25 no.12
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    • pp.1739-1748
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    • 2021
  • Real estate prices affect countries, businesses, and households, and many studies have been conducted on the real estate bubble in recent soaring real estate prices. However, if the real estate bubble prediction simply compares the real estate price, or if it does not reflect key psychological variables in real estate sales, it can be judged that the accuracy of the bubble prediction model is poor. The purpose of this study is to design a predictive model that can explain the real estate bubble situation by region using the autoencoder technique. Existing real estate bubble analysis studies failed to set various types of variables that affect prices, and most of them were conducted based on linear models. Thus, this study suggests the possibility of introducing techniques and variables that have not been used in existing real estate bubble studies.

A Exploratory Study on the Determinants Predicting Student Depature of Freshmen: Focusing on the Case of S University (대학 신입생 중도탈락 예측 요인 분석: S대학 사례를 중심으로)

  • Lee, Eun-jung;Lee, Jeong-hun
    • The Journal of the Korea Contents Association
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    • v.21 no.4
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    • pp.317-330
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    • 2021
  • This study aims to derive the main factors for predicting student departure of university freshmen and provide the basis for establishing policies to prevent student departure at the institutional level. For this purpose, a random forest model is developed with the data observed for 2 years at a four-year private university in Seoul. In the prediction model, 6 variables of school adjustment factors and 12 variables of institution satisfaction factors are applied. The top 6 variables presenting the highest MDA turn out to be emotional stability, financial conditions, assurance in the choice of major, satisfaction with the choice of university, educational method(systematic teaching method), educational method(effectiveness of major education). Based on the results of this study, it is suggested the necessity of institutional design supporting freshmen to adapt to university life and stably continue their studies.

LSTM-Auto-encoder for Renewable Energy Generation Forecast (재생에너지 발전량 예측을 위한 LSTM-Autoencoder)

  • Gun-Ha Park;Jong-Chan Kim
    • The Journal of the Korea institute of electronic communication sciences
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    • v.19 no.6
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    • pp.1225-1230
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    • 2024
  • This paper conducted a study to derive the predicted value of power generation based on the renewable energy generation data. In order to measure the power generation, multivariate variables such as power generation data and environmental data of renewable energy were measured. In order to secure the stability and reliability of the measured values, preprocessing was performed using the ARIMA (Auto Regressive Integrated Moving Average) model. In order to predict the power generation of renewable energy, the power generation was measured using the LSTM-Autoencoder combination model, a deep learning model, and the long-term dependency learning method of each multivariate variable was used to increase the prediction accuracy. The trend of environmental variables using refined data was more stable when the existing data were used as it was, and the accuracy of about 94% was measured by improving the renewable energy generation prediction algorithm by using only the highly correlated variable among the multivariate variables by reflecting the results of the correlation analysis.